A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters

For obtaining the more robust, novel, stable, and consistent clustering result, clustering ensemble has been emerged. There are two approaches in clustering ensemble frameworks: (a) the approaches that focus on creation or preparation of a suitable ensemble, called as ensemble creation approaches, and (b) the approaches that try to find a suitable final clustering (called also as consensus clustering) out of a given ensemble, called as ensemble aggregation approaches. The first approaches try to solve ensemble creation problem. The second approaches try to solve aggregation problem. This paper tries to propose an ensemble aggregator, or a consensus function, called as Robust Clustering Ensemble based on Sampling and Cluster Clustering (RCESCC).RCESCC algorithm first generates an ensemble of fuzzy clusterings generated by the fuzzy c-means algorithm on subsampled data. Then, it obtains a cluster-cluster similarity matrix out of the fuzzy clusters. After that, it partitions the fuzzy clusters by applying a hierarchical clustering algorithm on the cluster-cluster similarity matrix. In the next phase, the RCESCC algorithm assigns the data points to merged clusters. The experimental results comparing with the state of the art clustering algorithms indicate the effectiveness of the RCESCC algorithm in terms of performance, speed and robustness.

Clusteringensemble .Fuzzyc-means .Between-clustersimilarity .Subsampling 1 Introduction